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Fetal Anomaly Detection in Ultrasound Image
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 129 - Number 9 |
| Year of Publication: 2015 |
| Authors: Athira P.K., Linda Sara Mathew |
10.5120/ijca2015906587
|
Athira P.K., Linda Sara Mathew . Fetal Anomaly Detection in Ultrasound Image. International Journal of Computer Applications. 129, 9 ( November 2015), 1-4. DOI=10.5120/ijca2015906587
Abstract
Ultrasound is one of the most popular medical imaging technologies that can help a physician evaluate, diagnose and treat medical conditions. Although ultrasound imaging is generally considered good medical tool but the overall detection rate of Congenital Heart Defects (CHD) using ultrasound image remain anomic. Congenital Heart Defects are the heart problem that occurs before birth. Recognizing Congenital Heart Defects at right time is a difficult task for Physicians due to lack of subject specialists or inexperience with the previous cases or even as the children they can’t express their problem in a proper way. In order to improve the diagnosis accuracy and to reduce the diagnosis time, it has become a demanding issue to develop an efficient and reliable medical Decision Support System. Hence machine learning approaches such as neural networks have shown great potential to be applied in the development of medical Decision Support System for Heart Disease. Fetal anomaly detection mainly carried out in four steps. Noise removal, segmentation, feature extraction and classification.
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